Using psychometric analysis for determining credit risk

ABSTRACT

A method of obtaining psychometric information about a user is disclosed. A psychometric graphical object is provided to the user by a personalized user management module. User interaction with the psychometric graphical object is measured by a psychological traits evaluation module. At least a first wider portion of the psychometric graphical object is labeled with a first keyword related to a psychological trait. At least a second wider portion of the psychometric graphical object is labeled with a second keyword related to the psychological trait.

RELATED APPLICATIONS

This United States (U.S.) patent application is a continuation andclaims the benefit of U.S. patent application Ser. No. 15/704,586;titled USING PSYCHOMETRIC ANALYSIS FOR DETERMINING CREDIT RISK filed onFeb. 14, 2014 by inventors John Buckwalter et al., now allowed. U.S.patent application Ser. No. 15/704,586 is a continuation and claims thebenefit of U.S. Utility application Ser. No. 14/577,866, filed on Dec.19, 2014 by inventors John Buckwalter et al, entitled USING PSYCHOMETRICANALYSIS FOR DETERMINING CREDIT RISK, pending; the entire contents ofwhich is incorporated herein by reference for all intents and purposes.

BACKGROUND OF THE INVENTION

Credit scores are widely used by lenders because they are inexpensiveand largely accepted by consumers and lenders. However, they do have anumber of drawbacks. For example, studies have shown that the FICO (FairIsaac Corporation) score is not always a good predictor of credit risk.Studies have also shown that the accuracy of FICO in predictingdelinquency has diminished in recent years. In addition, there are waysfor a consumer to game the FICO system. Therefore, improved techniquesfor predicting credit risk of an individual would be desirable.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments of the invention are disclosed in the followingdetailed description and the accompanying drawings.

FIG. 1 is a block diagram illustrating an embodiment of a personalizeduser interaction system 106 for personalizing the interactions with aborrower or potential borrower.

FIG. 2 is a flowchart illustrating an embodiment of a process 200 forpersonalizing the interactions with a user who is seeking to obtain aloan or credit or who currently has a loan or credit which is managed ormonitored by the system.

FIG. 3 illustrates one embodiment of an application of psychometricassessments to business flow.

FIGS. 4A-4E illustrate an embodiment of a plurality of hourglass shapedpsychometric graphical objects that can be used to evaluate differentpersonality traits of the users.

FIG. 5 illustrates a questionnaire that is based upon the Ten-ItemPersonality Inventory (TIPI).

FIG. 6A illustrates an embodiment of an hourglass shaped psychometricgraphical object 602 related to conscientiousness.

FIG. 6B illustrates the distribution of the click points on thehourglass object for different users having differentTIPI-conscientiousness (TIPI-C) scores.

FIG. 6C illustrates the probability distribution (i.e., normalizedhistogram) of the results shown in FIG. 6B along the x-axis.

FIG. 6D illustrates the probability distribution of the results shown inFIG. 6B along the y-axis.

FIG. 7 illustrates an embodiment of an hourglass shaped psychometricgraphical object 702 related to openness.

FIG. 8 illustrates an embodiment of an hourglass shaped psychometricgraphical object 802 related to extraversion.

FIG. 9 illustrates an embodiment of an hourglass shaped psychometricgraphical object 902 related to agreeableness.

FIG. 10 illustrates an embodiment of an hourglass shaped psychometricgraphical object 1002 related to neuroticism.

FIG. 11A illustrates the distribution of the click points on hourglassobject 602 for different users who have reported themselves as having alow credit score or a high credit score.

FIG. 11B illustrates the probability distribution (i.e., normalizedhistogram) of the results shown in FIG. 11A along the x-axis.

FIG. 11C illustrates the probability distribution of the results shownin FIG. 11A along the y-axis.

FIGS. 12A-12E illustrate an embodiment of a set of five psychometricgraphical objects (1202, 1204, 1206, 1208, and 1210) that can be used toevaluate spending patterns of the users.

FIG. 13 shows that users who have low credit scores (e.g., with creditscores lower than 500) on average have a spending pattern that isclosest to spending curve 1202.

FIG. 14A illustrates a risk-reward curve 1402 that is a logarithmiccurve.

FIG. 14B illustrates a risk-reward curve 1404 that is a linear curve.

FIG. 14C illustrates a risk-reward curve 1406 that is an exponentialcurve.

FIG. 15A illustrates the distribution of the click points on risk-rewardcurve 1402 for users who have reported themselves as having low creditscores or high credit scores.

FIG. 15B illustrates the distribution of the click points on risk-rewardcurve 1404 for users who have reported themselves as having low creditscores or high credit scores.

FIG. 15C illustrates the distribution of the click points on risk-rewardcurve 1406 for users who have reported themselves as having low creditscores or high credit scores.

FIG. 16 illustrates the probability distribution of individuallynormalized scores.

DETAILED DESCRIPTION

The invention can be implemented in numerous ways, including as aprocess; an apparatus; a system; a composition of matter; a computerprogram product embodied on a computer readable storage medium; and/or aprocessor, such as a processor configured to execute instructions storedon and/or provided by a memory coupled to the processor. In thisspecification, these implementations, or any other form that theinvention may take, may be referred to as techniques. In general, theorder of the steps of disclosed processes may be altered within thescope of the invention. Unless stated otherwise, a component such as aprocessor or a memory described as being configured to perform a taskmay be implemented as a general component that is temporarily configuredto perform the task at a given time or a specific component that ismanufactured to perform the task. As used herein, the term ‘processor’refers to one or more devices, circuits, and/or processing coresconfigured to process data, such as computer program instructions.

A detailed description of one or more embodiments of the invention isprovided below along with accompanying figures that illustrate theprinciples of the invention. The invention is described in connectionwith such embodiments, but the invention is not limited to anyembodiment. The scope of the invention is limited only by the claims andthe invention encompasses numerous alternatives, modifications andequivalents. Numerous specific details are set forth in the followingdescription in order to provide a thorough understanding of theinvention. These details are provided for the purpose of example and theinvention may be practiced according to the claims without some or allof these specific details. For the purpose of clarity, technicalmaterial that is known in the technical fields related to the inventionhas not been described in detail so that the invention is notunnecessarily obscured.

Traditionally, consumers who want to obtain a mortgage, consolidateloans or credit card debt, or borrow for home improvement or a wedding,would visit a local bank for a personal loan. But increasingly,consumers are finding alternatives to traditional banking from onlinelenders or online marketplaces.

Traditional banks, credit card companies, and online lenders use anunderwriting process to assess the eligibility of a customer to receiveits products, including mortgages or credit. For example, these lendersuse credit scores to evaluate the potential risk posed by lending moneyto different consumers. A credit score is a number representing thecreditworthiness of a person, i.e., the likelihood that person will payhis or her debts. Credit scores include the FICO (Fair IsaacCorporation) score, Vantage Score, CE Score, and the like.

The FICO model is used by the vast majority of banks and creditgrantors, and is based on consumer credit files of the three nationalcredit bureaus: Experian, Equifax, and TransUnion. The FICO score isdesigned to measure the risk of default by taking into account variousfactors in a person's financial history, including payment history, debtburden, length of credit history, types of credit used, and any recentsearches for credit. There are several types of FICO credit score:classic or generic, bankcard, personal finance, mortgage, installmentloan, auto loan, and NextGen score. The generic or classic FICO score isbetween 300 and 850. Higher scores indicate lower credit risk.

Credit scores are widely used by lenders because they are inexpensiveand largely accepted by consumers and lenders. However, they do have anumber of drawbacks. For example, studies have shown that FICO is notalways a good predictor of credit risk. Studies have also shown that theaccuracy of FICO in predicting delinquency has diminished in recentyears. In addition, there are ways for a consumer to game the FICOsystem.

As will be described in greater detail below, psychometrics may be usedto enhance the underwriting process for loans and credit. Psychologicaltraits evaluation may be used to enhance the prediction of credit riskof a particular borrower. For example, using machine learning processesor logistic regression, psychological traits evaluation may be used toclassify potential borrowers into different categories, from the lowestpredicted credit risk to the highest predicted credit risk. Thepsychological traits evaluation may be used at different stages of theunderwriting process. For example, the psychological traits evaluationmay be used during an initial screening process or customeridentification process, in which only potential borrowers having apredicted credit risk below a certain threshold are considered furtherin the underwriting process. The psychological traits evaluation mayalso be used after a loan has been underwritten. For example, thepsychological traits evaluation may be used to continuously guide aborrower, determine how to interact with, provide feedback to, andmanage the borrower in a variety of settings, e.g., customer service,training, user engagement, and marketing, in a personalized manner,thereby helping the borrower to alter his/her changeable behaviors tofurther reduce his/her credit risk. In some embodiments, an iconographicpsychometric evaluation may be used to provide a psychological traitsevaluation for a potential borrower or borrower rapidly, efficiently,and accurately. In contrast to the questionnaire type of psychometricevaluations, which are tedious, lengthy, and boring, the iconographicpsychometric evaluation can be quickly performed and is highly engaging,thereby capturing the borrower's attention.

FIG. 1 is a block diagram illustrating an embodiment of a personalizeduser interaction system 106 for personalizing the interactions with aborrower or potential borrower. Personalized user interaction system 106includes a personalized user management module 108, a psychologicaltraits evaluation module 110, a credit risk evaluation module 112, andone or more databases for storing psychological traits evaluation andcredit risk evaluation data collected by psychological traits evaluationmodule 110 and credit risk evaluation module, respectively. A pluralityof user devices 102 may connect to personalized user interaction system106 via a network 104. Network 104 may be any combination of public orprivate networks, including intranets, local-area networks (LANs),wide-area networks (WANs), and the Internet. User devices 102 mayinclude desktop computers, laptops, tablets, smartphones or othercomputing devices used by potential borrowers or borrowers communicatingwith personalized user interaction system 106. When a user device 102 isconnected to personalized user interaction system 106, a graphicalinterface may be provided by the system such that the user may browsethrough different pages, log into an account, or interact with thesystem in different ways.

FIG. 2 is a flowchart illustrating an embodiment of a process 200 forpersonalizing the interactions with a user who is seeking to obtain aloan or credit or who currently has a loan or credit that is managed ormonitored by the system. In some embodiments, process 200 is performedby the various modules of personalized user interaction system 106 asshown in FIG. 1.

With continued reference to FIG. 1 and FIG. 2, at 202, personalized usermanagement module 108 presents a plurality of psychometric graphicalobjects to a user that is using a user device 102 to connect topersonalized user interaction system 106. The psychometric graphicalobjects may include icons, images, graphs, plots, pie-charts, lines, andthe like.

At 204, in response to the plurality of psychometric graphical objectsthat are presented to the user, the user may interact with the pluralityof psychometric graphical objects. For example, the user may click on aportion of a psychometric graphical object, drag on a portion of apsychometric graphical object, select one of many presented psychometricgraphical objects, or hover the mouse or other pointing device over acertain area of a psychometric graphical object. Psychological traitsevaluation module 110 may measure the user's interaction with theplurality of psychometric graphical objects. Psychological traitsevaluation module 110 may measure different types of user interactioninformation, including the location (e.g., the (x, y) coordinates) ofthe portion of the psychometric graphical object that the user clickson, how long the user's mouse hovers over the psychometric graphicalobject, how long the user takes to select a particular psychometricgraphical object, and the like. The measured user's interaction may beoptionally stored in database 114.

At 206, psychological traits evaluation module 110 may evaluate aplurality of psychological traits of the user based on the measured userinteraction with the plurality of psychometric graphical objects.Different types of psychological traits may be evaluated based on themeasured user interaction with different types of psychometric graphicalobjects. One type of psychological traits includes the Big Fivepersonality traits, which are characteristics related to the five coredimensions of personality, including openness, conscientiousness,extraversion, agreeableness, and neuroticism. Another type ofpsychological traits includes characteristics related to a user'srisk-reward tolerance. Another type of psychological traits includescharacteristics related to a user's spending behaviors.

At 208, credit risk evaluation module 112 receives the evaluatedplurality of psychological traits of the user either directly frompsychological evaluation module 110 or from database 114, and determinesa weighted sum of the evaluated plurality of psychological traits of theuser. The weighted sum may be determined by machine learning processesor logistic regression. The weighted sum may be used as a metric forclassifying the user into one of many categories, each category having adifferent level of predicted credit risk. For example, the firstcategory has the lowest predicted credit risk; the second category has ahigher predicted credit risk, and so on.

At 210, personalized user management module 108 may personalize anyinteractions between the system and the user during an underwritingprocess. The personalization is based on the weighted sum or thepredicted credit risk of the user. The underwriting process includesmultiple phases. FIG. 3 illustrates one embodiment of an application ofpsychometric assessments to business flow. Initially, the psychometricassessments may be used for market segmentation. During this phase, theuser has not been qualified for a loan or a credit. During this phase,personalized user management module 108 may identify the user as apotential customer based on the user's predicted credit risk. Thepersonalized interactions may include sending the user an invitation toopen an initial account with the system, sending the user marketinginformation about programs that may be suitable to her, and the like. Inanother phase, the user has already been qualified for a loan or acredit through the system. During this phase, personalized usermanagement module 108 may provide on-going personalized customer supportand guidance to the user based on the user's psychometric evaluation andpredicted credit risk. The personalized interactions may include sendingthe user feedbacks about his/her financial actions or decisions, sendingalerts to the user regarding payment deadlines, sendingfinancial-related tips and information to the user, and the like. Thesepersonalized interactions may help the user to alter his/her changeablebehaviors to further reduce his/her credit risk.

Different types of psychometric graphical objects may be used toevaluate different types of psychological traits. In some embodiments,hourglass shaped psychometric graphical objects are used to evaluatedifferent personality traits of the users. In some embodiments, aplurality of psychometric graphical objects representing risk-to-rewardratios is used to evaluate the risk-reward tolerance or investment styleof the users. In some embodiments, a plurality of psychometric graphicalobjects representing the schedule of spending over time is used toevaluate spending style and behaviors of the users.

In some embodiments, psychometric graphical objects are used to evaluatedifferent personality traits of the users. The personality traits may berelated to different dimensions of an individual. For example, somedimensions are core traits that remain largely unchanged throughout anindividual's life. Some dimensions are based on learning experience, andare more likely to change based on the life experiences and events ofthe individual. Some personality traits are related to the five coredimensions of personality (also referred to as the Big Five personalitytraits), including openness, conscientiousness, extraversion,agreeableness, and neuroticism. For example, personality traits relatedto openness include imagination and insight. Personality traits relatedto conscientiousness include a high level of thoughtfulness, goodimpulse control, and goal-directed behavior. Personality traits relatedto extraversion include excitability, sociability, talkativeness,assertiveness, and a high degree of emotional expressiveness.Personality traits related to agreeableness include trust, altruism,kindness, affection, and other pro-social behaviors. Personality traitsrelated to neuroticism include emotional instability, anxiety,moodiness, irritability, and sadness. Note that the various personalitytraits described above are provided for illustration purposes only;accordingly, the present application is not limited to the abovedescribed personality traits only.

FIGS. 4A-4E illustrate an embodiment of a plurality of hourglass shapedpsychometric graphical objects that can be used to evaluate differentpersonality traits of the users. FIG. 5 illustrates a questionnaire thatis based upon the Ten-Item Personality Inventory (TIPI). The TIPI wasdesigned to assess the traits defined by the Big Five personalitytraits—openness, conscientiousness, extraversion, agreeableness, andneuroticism. As will be described in greater detail below, theevaluation results derived from users interacting with the hourglassshaped psychometric graphical objects as shown in FIGS. 4A-4E correlatewell with the evaluation results derived from users answering the TIPIten-item questionnaire as shown in FIG. 5. However, one of theadvantages of using the psychometric graphical objects as shown in FIGS.4A-4E is that it can evaluate the psychological traits of a potentialborrower or borrower rapidly, efficiently, and accurately. In addition,the experience provided to the user is highly visual, engaging, andintuitive, thereby capturing the user's attention. In contrast, the TIPIten-item questionnaire as shown in FIG. 5 requires the user to read alarger amount of text, which is tedious, lengthy, and boring.Furthermore, the choices provided by the TIPI ten-item questionnaire tothe users to choose from are discrete with a coarse level ofgranularity, while the choices provided by the hourglass shapedpsychometric graphical objects to the users are continuous with a muchfiner level of granularity (e.g., pixel level of granularity).

FIGS. 4A-4E illustrate five hourglass shaped psychometric graphicalobjects (402, 404, 406, 408, and 410) for a user to interact with.Object 402 may be used to evaluate the personality trait of openness.Object 404 may be used to evaluate the personality trait ofextraversion. Object 406 may be used to evaluate the personality traitof agreeableness. Object 408 may be used to evaluate the personalitytrait of neuroticism. Object 410 may be used to evaluate the personalitytrait of conscientiousness.

Each of the hourglass shaped psychometric graphical objects as shown inFIGS. 4A-4E correspond to two of the questions in the TIPI ten-itemquestionnaire as shown in FIG. 5. For example, object 402 corresponds toquestion 5 and question 10 of the questionnaire. In question 5, the useris asked to rank how strongly disagree or agree that the user viewshimself/herself as being “open to new experiences” or “complex.” Inquestion 7, the user is asked to rank how strongly disagree or agreethat the user views himself/herself as being “conventional” or“uncreative”. As being “open to new experiences” or “complex” is verydissimilar to or opposite from being “conventional” or“straight-forward,” a user who ranks himself highly (i.e., agreestrongly) in the former two traits would likely rank himself lower inthe latter two traits. Similarly, a user who ranks himself lower in theformer two traits would likely rank himself higher in the latter twotraits. In each of the questions, the user is asked to rankhimself/herself according to seven discrete levels—disagree strong,disagree moderately, disagree a little, neither agree/disagree, agree alittle, agree moderately, and agree strongly.

In contrast, each of the hourglass shaped psychometric graphical objectsallows a user to click on a continuous scale, thereby providing the usera much finer level of granularity of choices to choose from. Forexample, object 402 displays the keywords “open” and “complex” at thetop and widest part of the hourglass and the keywords “conventional” and“straight-forward” at the bottom and widest part of the hourglass, andthe user is allowed to click on any part of hourglass psychometricgraphical object 402 such that the x and y coordinates of the clickpoint can be determined and recorded. The hourglass shape is wider atthe top base and at the bottom base, but narrower in the middle. It isalso symmetric along the x-axis and the y-axis. These characteristicsvisually invite the user to click along a centered vertical line thatdivides the hourglass shape equally. One of the advantages of the methodis that it encourages more consistent user behavior when the user isinteracting with the graphical objects. In some embodiments, thehourglass shape's narrowest part has lighter shading. Thischaracteristic helps a user to visualize the middle of the object, whichrepresents a neutral trait. One of the advantages of the method is thatit gives the user a better sense perceptually of how far or how close heis associating himself with a particular trait. In addition, only fivemouse clicks (one click on each hourglass object) are required toevaluate the traits defined by the Big Five personality traits—openness,conscientiousness, extraversion, agreeableness, and neuroticism.Therefore, the evaluation for a particular user can be performed in arelatively short period of time.

The evaluation results derived from users interacting with the hourglassshaped psychometric graphical objects as shown in FIGS. 4A-4E correlatewell with the evaluation results derived from users answering the TIPIten-item questionnaire as shown in FIG. 5. For example, results in FIGS.4A-4E show that users who score higher in the openness scale 403 basedon the TIPI ten-item questionnaire tend to click on the upper portion ofthe hourglass object 402, the portion that is closer on the y-axis tothe words “open” and “complex.”

FIGS. 6A-6D show another illustrative example that the results derivedfrom users interacting with a hourglass shaped psychometric graphicalobject related to conscientiousness correlate well with the evaluationresults derived from users answering the TIPI questions that are relatedto conscientiousness. FIG. 6A illustrates an embodiment of an hourglassshaped psychometric graphical object 602 related to conscientiousness.FIG. 6B illustrates the distribution of the click points on thehourglass object for different users having differentTIPI-conscientiousness (TIPI-C) scores. FIG. 6C illustrates theprobability distribution (i.e., normalized histogram) of the resultsshown in FIG. 6B along the x-axis. FIG. 6D illustrates the probabilitydistribution of the results shown in FIG. 6B along the y-axis. Users whoscore higher in the TIPI-conscientiousness scale (e.g., TIPI-Cscore=6.0) tend to click on the upper portion of the hourglass object.Therefore, the evaluation of the psychological traits of users based onthe hourglass shaped psychometric graphical objects is accurate withfiner granularity, in addition to being efficient, highly visual,engaging, and intuitive.

FIG. 7 illustrates an embodiment of an hourglass shaped psychometricgraphical object 702 related to openness. FIG. 8 illustrates anembodiment of an hourglass shaped psychometric graphical object 802related to extraversion. FIG. 9 illustrates an embodiment of anhourglass shaped psychometric graphical object 902 related toagreeableness. FIG. 10 illustrates an embodiment of an hourglass shapedpsychometric graphical object 1002 related to neuroticism.

FIGS. 11A-11C illustrate that the evaluated psychological traits of theusers are correlated with the users' credit risk. FIG. 11A illustratesthe distribution of the click points on hourglass object 602 fordifferent users who have reported themselves as having low credit scoresor high credit scores. FIG. 11B illustrates the probability distribution(i.e., normalized histogram) of the results shown in FIG. 11A along thex-axis. FIG. 11C illustrates the probability distribution of the resultsshown in FIG. 11A along the y-axis. In particular, curve 1104 is theprobability distribution of users with high credit scores along they-axis. Curve 1106 is the probability distribution of users with lowcredit scores along the y-axis. As shown in curve 1104 of FIG. 11C,users who report higher credit scores tend to click on the upper portionof the hourglass object, i.e., they tend to have higher scores ofconscientiousness. As shown in FIG. 11C, there is a significantdifference in the area under curves 1104 and 1106 when y is below athreshold 1102. Therefore, if the users who have a conscientiousnessscore below threshold 1102 are eliminated, a larger portion of the userswith low credit scores are eliminated as compared to those with highcredit scores. This shows that the evaluated psychological traits of theusers may be used to predict the users' credit risk.

In some embodiments, psychometric graphical objects are used to evaluatethe spending style and behavior of the users. FIGS. 12A-12E illustratean embodiment of a set of five psychometric graphical objects (1202,1204, 1206, 1208, and 1210) that can be used to evaluate the spendingpatterns of the users. Each of the graphical objects includes a spendingcurve. In particular, in each of the graphical objects, the amount spentis plotted along the y-axis and the number of days since the user'spaycheck is received is plotted along the x-axis. In FIG. 12A, spendingcurve 1202 shows that the user spends the most at the beginning of the30-day cycle, then spends less and less until the middle of the cycle,and then his spending remains at a very low level until the end of the30-day cycle. In FIG. 12B, spending curve 1204 shows that the userspends the most at the beginning of the 30-day cycle, then spends lessand less until the end of the 30-day cycle. In FIG. 12C, spending curve1206 shows that the user spends a constant amount throughout the 30-daycycle. In FIG. 12D, spending curve 1208 shows that the user spends theleast at the beginning of the 30-day cycle, then spends more and moreuntil the end of the 30-day cycle. In FIG. 12E, spending curve 1210shows that the user spends the least at the beginning of the 30-daycycle, and then his spending remains at a very low level until themiddle of the cycle, and then his spending increases rapidly until theend of the 30-day cycle.

Users are presented the above-mentioned five spending curves and thenthey are asked to select by clicking the spending curve that bestrepresents how they spend their monthly paycheck after their bills arepaid. Each of the spending curves are assigned a score, e.g., from oneto five. FIG. 13 illustrates that the spending curve that the user picksis correlated with the user's credit risk. For example, FIG. 13 showsthat users who have low credit scores (e.g., with credit scores lowerthan 500) on average have a spending pattern that is closest to spendingcurve 1202. In other words, the users who have low credit scores onaverage spend the most at the beginning of the 30-day cycle, then spendless and less until the middle of the cycle, and then spending remainsat a very low level until the end of the 30-day cycle. FIG. 13 alsoshows that users who have high credit scores (e.g., with credit scoreshigher than 700) on average have a spending pattern that is closest tospending curve 1206. These users on average spend a constant amountthroughout the 30-day cycle. This shows that the evaluated spendingstyle and behaviors of the users may be used to predict the users'credit risks.

In some embodiments, psychometric graphical objects are used to evaluaterisk-reward tolerance or the investment style of the users. FIGS.14A-14C illustrate an embodiment of a set of three psychometricgraphical objects (1402, 1404, and 1406) that can be used to evaluaterisk-reward tolerance of the users. Each of the graphical objectsincludes a risk-reward curve. In particular, in each of the graphicalobjects, the amount of reward is plotted along the y-axis and the amountof risk involved is plotted along the x-axis. In FIG. 14A, risk-rewardcurve 1402 is a logarithmic curve. With a logarithmic risk-rewardrelationship, moderate early risk is required in order to obtainincreasing reward. In FIG. 14B, risk-reward curve 1404 is a linearcurve. With a linear risk-reward relationship, a constant increase ofrisk is required in order to obtain constant increase of reward. In FIG.14C, risk-reward curve 1406 is an exponential curve. With an exponentialrisk-reward relationship, a large amount of early risk is required inorder to obtain increasing reward.

Users are presented the above-mentioned three risk-reward curves andthey are asked to click on one point on each of the risk-reward curvesthat best represents their preferred risk-reward combination given theparticular risk-reward relationship. FIG. 15A illustrates thedistribution of the click points on risk-reward curve 1402 for users whohave reported themselves as having low credit scores or high creditscores. FIG. 15B illustrates the distribution of the click points onrisk-reward curve 1404 for users who have reported themselves as havinglow credit scores or high credit scores. FIG. 15C illustrates thedistribution of the click points on risk-reward curve 1406 for users whohave reported themselves as having low credit scores or high creditscores. A score may be assigned to the user based on how the user clickson each of the risk-reward curves.

FIG. 16 illustrates that the evaluated risk-reward tolerances of theusers are correlated with the users' credit risk. FIG. 16 illustratesthe probability distribution of individually normalized scores. There isa significant tendency for low credit score users (i.e., those with highcredit risk) to have either very low or very high reward to risktolerance. This shows that the evaluated risk-reward tolerance of theusers may be used to predict the users' credit risk.

Because the evaluated plurality of psychological traits of a user ispredictive of the user's credit risk, a weighted sum of the evaluatedplurality of psychological traits of the user may be used as a metricfor classifying the user into one of many categories, each categoryhaving a different level of predicted credit risk. The weighted sum maybe determined by machine learning processes or regression (both linearand logistic) models.

In some embodiments, a credit risk score may be determined as shownbelow:

Predicted Credit Risk Score=Intercept+W ₁*HourGlassO+W ₂*HourGlassC+W₃*HourGlassE+W ₄*HourGlassA+W ₅*HourGlassN+W ₆*RiskRewardComputedScore+W₇*SpendingCurveSelection

-   where Wn=regression determined weighting value for the particular    attribute (e.g. HourGlassE)    -   HourGlassO=score corresponding to the hourglass related to        openness    -   HourGlasssC=score corresponding to the hourglass related to        conscientiousness    -   HourGlassE=score corresponding to the hourglass related to        extraversion    -   HourGlassA=score corresponding to the hourglass related to        agreeableness    -   HourGlassN=score corresponding to the hourglass related to        neuroticism    -   RiskRewardComputedScore=score corresponding to the risk-reward        curves    -   SpendingCurveSelection=score corresponding to the spending        curves

Although the foregoing embodiments have been described in some detailfor purposes of clarity of understanding, the invention is not limitedto the details provided. There are many alternative ways of implementingthe invention. The disclosed embodiments are illustrative and notrestrictive.

What is claimed is:
 1. A method of obtaining psychometric informationabout a user, comprising: providing a psychometric graphical object tothe user by a personalized user management module; and measuring userinteraction with the psychometric graphical object by a psychologicaltraits evaluation module; and wherein at least a first wider portion ofthe psychometric graphical object is labeled with a first keywordrelated to a psychological trait, and wherein at least a second widerportion of the psychometric graphical object is labeled with a secondkeyword related to the psychological trait.
 2. The method of claim 1,wherein the first keyword and the second keyword each describes thepsychological trait, and wherein the first keyword and the secondkeyword are dissimilar to each other.
 3. The method of claim 1, whereinthe psychological trait comprises a personality trait.
 4. The method ofclaim 3, wherein the personality trait comprises a Big Five personalitytrait.
 5. The method of claim 1, wherein the psychometric graphicalobject further includes a feature between the first wider portion andthe second wider portion that facilitates visualization of a portionthat is between the first wider portion and the second wider portion. 6.The method of claim 5, wherein the feature comprises a shading that isdifferent from a shading of the first wider portion and a shading of thesecond wider portion.
 7. The method of claim 1, wherein the psychometricgraphical object further includes a narrower portion that is between thefirst wider portion and the second wider portion.
 8. The method of claim1, wherein the psychometric graphical object comprises a hourglassshape, and wherein the first wider portion of the psychometric graphicalobject comprises a first base of the hourglass shape, and wherein thesecond wider portion of the psychometric graphical object comprises asecond base of the hourglass shape.
 9. The method of claim 1, whereinthe psychometric graphical object comprises a shape that is symmetricalalong the x-axis and the y-axis.
 10. The method of claim 1, whereinmeasuring user interaction with the psychometric graphical objectscomprises measuring a position of where the user clicks on thepsychometric graphical object.
 11. The method of claim 10, whereinmeasuring the position comprises measuring at a pixel level.
 12. Asystem for obtaining psychometric information about a user, comprising:a personalized user management module configured to provide apsychometric graphical object to the user; and a psychological traitsevaluation module configured to measure user interaction with thepsychometric graphical object; and wherein at least a first widerportion of the psychometric graphical object is labeled with a firstkeyword related to a psychological trait, and wherein at least a secondwider portion of the psychometric graphical object is labeled with asecond keyword related to the psychological trait.
 13. The system ofclaim 12, wherein the first keyword and the second keyword eachdescribes the psychological trait, and wherein the first keyword and thesecond keyword are dissimilar to each other.
 14. The system of claim 12,wherein the psychological trait comprises a personality trait.
 15. Thesystem of claim 14, wherein the personality trait comprises a Big Fivepersonality trait.
 16. The system of claim 12, wherein the psychometricgraphical object further includes a feature between the first widerportion and the second wider portion that facilitates visualization of aportion that is between the first wider portion and the second widerportion.
 17. The system of claim 16, wherein the feature comprises ashading that is different from a shading of the first wider portion anda shading of the second wider portion.
 18. The system of claim 12,wherein the psychometric graphical object further includes a narrowerportion that is between the first wider portion and the second widerportion.
 19. The system of claim 12, wherein the psychometric graphicalobject comprises a hourglass shape, and wherein the first wider portionof the psychometric graphical object comprises a first base of thehourglass shape, and wherein the second wider portion of thepsychometric graphical object comprises a second base of the hourglassshape.
 20. The system of claim 12, wherein the psychometric graphicalobject comprises a shape that is symmetrical along the x-axis and they-axis.
 21. The system of claim 12, wherein measuring user interactionwith the psychometric graphical objects comprises measuring a positionof where the user clicks on the psychometric graphical object.
 22. Thesystem of claim 21, wherein measuring the position comprises measuringat a pixel level.
 23. A computer program product for obtainingpsychometric information about a user, the computer program productbeing embodied in a non-transitory computer readable storage medium andcomprising computer instructions for: providing a psychometric graphicalobject to the user by a personalized user management module; andmeasuring user interaction with the psychometric graphical object by apsychological traits evaluation module; and wherein at least a firstwider portion of the psychometric graphical object is labeled with afirst keyword related to a psychological trait, and wherein at least asecond wider portion of the psychometric graphical object is labeledwith a second keyword related to the psychological trait.